PD9478 Sample Summary

## `summarise()` has grouped output by 'patient', 'age_at_sample_exact', 'age_at_sample', 'DOB', 'DATE_OF_DIAGNOSIS'. You can override using the `.groups` argument.
## Joining, by = "PDID"
patient ID age_at_sample_exact cell_type phase BaitLabel
2 PD9478 PD9478dd 54.17112 BM Gran Recapture PD9478dd
3 PD9478 PD9478de 54.80630 PB Gran Recapture PD9478de
4 PD9478 PD9478df 55.37851 PB Gran Recapture PD9478df
5 PD9478 PD9478dg 61.71663 PB Gran Recapture PD9478dg
6 PD9478 PD9478dh 63.23066 PB Gran Recapture PD9478dh
7 PD9478 PD9478di 63.99726 PB Gran Recapture PD9478di
8 PD9478 PD9478dj 66.42847 PB Gran Recapture PD9478dj
9 PD9478 PD9478dk 66.96783 PB Gran Recapture PD9478dk
10 PD9478 PD9478dl 67.57016 PB Gran Recapture PD9478dl
11 PD9478 PD9478dm 68.02190 PB Gran Recapture PD9478dm
1 PD9478 COLONY68 68.02464 BFU-E-Colony Colony NA
12 PD9478 PD9478dn 70.03422 PB Gran Recapture PD9478dn
13 PD9478 PD9478do 70.51335 PB Gran Recapture PD9478do
14 PD9478 PD9478dp 71.04723 BM Gran Recapture PD9478dp

Tree

tree=plot_basic_tree(PD$pdx,label = PD$patient,style="classic")

Expanded Tree with Node Labels

The nodes in this plot can be cross-referenced with nodes specified in subsequent results. The plot also serves to give an idea of what the topology at the top of the tree looks like.

tree=plot_basic_tree(expand_short_branches(PD$pdx,prop = 0.1),label = PD$patient,style="classic")
node_labels(tree)

Timing of driver mutations (using Model = poisson_tree )

Note that the different colours on the tree indicate the separately fitted mutation rate clades.

Driver Specific Mutation Rates & Telomere Lengths by Colony & Timepoint

## 
## Random-Effects Model (k = 1; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   0.0000   -0.0000    4.0000      -Inf   16.0000   
## 
## tau^2 (estimated amount of total heterogeneity): 0
## tau (square root of estimated tau^2 value):      0
## I^2 (total heterogeneity / total variability):   0.00%
## H^2 (total variability / sampling variability):  1.00
## 
## Test for Heterogeneity:
## Q(df = 0) = 0.0000, p-val = 1.0000
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  18.4230  0.5524  33.3516  <.0001  17.3403  19.5056  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
node driver status child_count type colony_count mean_lambda_rescaled correction sd_rescaled lb_rescaled ub_rescaled median_rescaled p_lt_wt
-1 WT 1 -1 local 8 18.42296 1.016185 0.3051312 17.84271 19.04460 18.41553 NA
85 JAK2 1 72 local 68 19.02647 1.016185 0.4951584 18.06907 20.01525 19.01747 0.155525
67 9pUPD:DNMT3A:JAK2 0 1 local 1 22.06799 1.016185 3.3529333 15.17648 28.91798 22.01345 0.115275
24 9pUPD:DNMT3A:JAK2 0 1 local 1 17.73788 1.016185 3.3688803 10.90467 25.01563 17.66711 0.606175
72 9pUPD:JAK2 0 1 local 1 21.40554 1.016185 2.8711780 15.67051 27.84762 21.12540 0.097375
3 9pUPD:DNMT3A:JAK2 0 1 local 1 21.71461 1.016185 3.3140179 14.88753 28.45361 21.66772 0.133775

Driver Acquisition Timeline

All ages are in terms of post conception years. The vertical red lines denote when colonies were sampled and blue lines when targeted follow up samples were taken.

patient node driver child_count lower_median upper_median lower_lb95 lower_ub95 upper_lb95 upper_ub95 N group age_at_diagnosis_pcy max_age_at_sample min_age_at_sample
PD9478 85 JAK2 72 0.0222469 10.05039 0.0149079 0.0438736 8.618587 11.62251 14 JAK2 53.76865 71.7755 54.89938
PD9478 86 DNMT3A 71 10.0503937 45.65464 8.6185874 11.6225101 44.425247 46.83687 14 DNMT3A 53.76865 71.7755 54.89938

Copy Number Variation and Timing

Summary of LOH timing inference

## Timings using the Clade Specific Rates
label node het.sensitivity chr start end nhet nhom mean_loh_event lower_loh_event upper_loh_event t_before_end t_before_end_lower t_before_end_upper kb count_in_bin count_se pmut pmut_se xmean xse_mean xsd x2.5. x50. x97.5. xn_eff xRhat lmean lse_mean patient driver3 child_count
9pUPD_A 67 0.7262 9 10469 34110846 2 0 NA NA NA NA NA NA 34100000 6765 82.25 0.01477 0.0001796 0.2903 0.0012951 0.2097 0.011412 0.2503 0.7573 26205 1 4.297 0.0003602 NA NA NA
9pUPD_B 24 0.8921 9 10469 35005250 1 0 NA NA NA NA NA NA 35000000 6891 83.01 0.01505 0.0001813 0.4367 0.0016423 0.2533 0.022671 0.4274 0.9014 23787 1 4.357 0.0003602 NA NA NA
9pUPD_C 72 0.8973 9 10469 34098110 12 8 NA NA NA NA NA NA 34000000 6753 82.18 0.01475 0.0001795 0.4779 0.0006041 0.1041 0.273510 0.4782 0.6782 29708 1 16.086 0.0011325 NA NA NA
9pUPD_D 3 0.6717 9 10469 34107232 5 0 NA NA NA NA NA NA 34100000 6765 82.25 0.01477 0.0001796 0.1605 0.0008847 0.1339 0.004867 0.1262 0.4950 22912 1 6.137 0.0005490 NA NA NA

Duplications?

VAF Distribution of Targeted Follow Up Samples

Here we exclude all local CNAs and depict as color VAF plots